Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations18835
Missing cells0
Missing cells (%)0.0%
Duplicate rows2205
Duplicate rows (%)11.7%
Total size in memory2.8 MiB
Average record size in memory157.1 B

Variable types

Text1
Numeric12
Categorical2

Alerts

Dataset has 2205 (11.7%) duplicate rowsDuplicates
acousticness is highly overall correlated with energyHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
time_signature is highly imbalanced (83.6%) Imbalance
song_popularity has 272 (1.4%) zeros Zeros
instrumentalness has 7150 (38.0%) zeros Zeros
key has 2182 (11.6%) zeros Zeros

Reproduction

Analysis started2024-12-15 16:26:39.736957
Analysis finished2024-12-15 16:27:24.159109
Duration44.42 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct13070
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-15T16:27:24.557803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length143
Median length92
Mean length16.617308
Min length1

Characters and Unicode

Total characters312987
Distinct characters308
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10174 ?
Unique (%)54.0%

Sample

1st rowBoulevard of Broken Dreams
2nd rowIn The End
3rd rowSeven Nation Army
4th rowBy The Way
5th rowHow You Remind Me
ValueCountFrequency (%)
2458
 
4.1%
the 1614
 
2.7%
feat 1496
 
2.5%
you 1051
 
1.8%
me 909
 
1.5%
i 735
 
1.2%
love 650
 
1.1%
my 542
 
0.9%
a 532
 
0.9%
to 514
 
0.9%
Other values (9918) 49458
82.5%
2024-12-15T16:27:25.508218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41124
 
13.1%
e 29073
 
9.3%
a 18940
 
6.1%
o 18724
 
6.0%
i 16006
 
5.1%
t 14461
 
4.6%
n 14373
 
4.6%
r 12900
 
4.1%
s 9650
 
3.1%
l 9621
 
3.1%
Other values (298) 128115
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41124
 
13.1%
e 29073
 
9.3%
a 18940
 
6.1%
o 18724
 
6.0%
i 16006
 
5.1%
t 14461
 
4.6%
n 14373
 
4.6%
r 12900
 
4.1%
s 9650
 
3.1%
l 9621
 
3.1%
Other values (298) 128115
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41124
 
13.1%
e 29073
 
9.3%
a 18940
 
6.1%
o 18724
 
6.0%
i 16006
 
5.1%
t 14461
 
4.6%
n 14373
 
4.6%
r 12900
 
4.1%
s 9650
 
3.1%
l 9621
 
3.1%
Other values (298) 128115
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41124
 
13.1%
e 29073
 
9.3%
a 18940
 
6.1%
o 18724
 
6.0%
i 16006
 
5.1%
t 14461
 
4.6%
n 14373
 
4.6%
r 12900
 
4.1%
s 9650
 
3.1%
l 9621
 
3.1%
Other values (298) 128115
40.9%

song_popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.991877
Minimum0
Maximum100
Zeros272
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2024-12-15T16:27:25.884950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median56
Q369
95-th percentile85
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.905654
Coefficient of variation (CV)0.41337759
Kurtosis-0.16910371
Mean52.991877
Median Absolute Deviation (MAD)14
Skewness-0.50148747
Sum998102
Variance479.85769
MonotonicityNot monotonic
2024-12-15T16:27:26.218817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 404
 
2.1%
52 389
 
2.1%
55 388
 
2.1%
60 383
 
2.0%
63 378
 
2.0%
62 370
 
2.0%
65 364
 
1.9%
64 364
 
1.9%
61 360
 
1.9%
69 359
 
1.9%
Other values (91) 15076
80.0%
ValueCountFrequency (%)
0 272
1.4%
1 111
0.6%
2 103
 
0.5%
3 72
 
0.4%
4 91
 
0.5%
5 83
 
0.4%
6 70
 
0.4%
7 78
 
0.4%
8 85
 
0.5%
9 61
 
0.3%
ValueCountFrequency (%)
100 12
 
0.1%
99 16
 
0.1%
98 47
0.2%
97 36
 
0.2%
96 53
0.3%
95 61
0.3%
94 85
0.5%
93 32
 
0.2%
92 62
0.3%
91 95
0.5%

song_duration_ms
Real number (ℝ)

Distinct11260
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.07677
Minimum-32768
Maximum32765
Zeros0
Zeros (%)0.0%
Negative9240
Negative (%)49.1%
Memory size36.9 KiB
2024-12-15T16:27:26.512086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-32768
5-th percentile-29507
Q1-15599.5
median613
Q316436.5
95-th percentile29418
Maximum32765
Range-3
Interquartile range (IQR)32036

Descriptive statistics

Standard deviation18738.093
Coefficient of variation (CV)45.694109
Kurtosis-1.1663965
Mean410.07677
Median Absolute Deviation (MAD)15958
Skewness-0.040086578
Sum7723796
Variance3.5111613 × 108
MonotonicityNot monotonic
2024-12-15T16:27:26.852976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-31608 25
 
0.1%
-16608 21
 
0.1%
-17204 20
 
0.1%
-7608 20
 
0.1%
15892 20
 
0.1%
13759 18
 
0.1%
20928 18
 
0.1%
-4436 17
 
0.1%
-1608 17
 
0.1%
-3126 16
 
0.1%
Other values (11250) 18643
99.0%
ValueCountFrequency (%)
-32768 2
 
< 0.1%
-32758 1
 
< 0.1%
-32755 1
 
< 0.1%
-32751 1
 
< 0.1%
-32744 2
 
< 0.1%
-32743 1
 
< 0.1%
-32740 1
 
< 0.1%
-32739 1
 
< 0.1%
-32738 4
 
< 0.1%
-32732 12
0.1%
ValueCountFrequency (%)
32765 1
 
< 0.1%
32762 2
 
< 0.1%
32752 9
< 0.1%
32747 3
 
< 0.1%
32738 2
 
< 0.1%
32733 1
 
< 0.1%
32728 1
 
< 0.1%
32712 7
< 0.1%
32698 1
 
< 0.1%
32696 3
 
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct3209
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25853902
Minimum1.02 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:27.182972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.02 × 10-6
5-th percentile0.000633
Q10.0241
median0.132
Q30.424
95-th percentile0.882
Maximum0.996
Range0.99599898
Interquartile range (IQR)0.3999

Descriptive statistics

Standard deviation0.28871891
Coefficient of variation (CV)1.1167324
Kurtosis-0.096275738
Mean0.25853902
Median Absolute Deviation (MAD)0.126
Skewness1.0711642
Sum4869.5824
Variance0.083358608
MonotonicityNot monotonic
2024-12-15T16:27:27.564503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 45
 
0.2%
0.135 44
 
0.2%
0.153 43
 
0.2%
0.102 41
 
0.2%
0.107 40
 
0.2%
0.0215 39
 
0.2%
0.161 38
 
0.2%
0.141 38
 
0.2%
0.128 37
 
0.2%
0.173 36
 
0.2%
Other values (3199) 18434
97.9%
ValueCountFrequency (%)
1.02 × 10-61
< 0.1%
1.36 × 10-61
< 0.1%
1.37 × 10-61
< 0.1%
1.4 × 10-61
< 0.1%
1.8 × 10-61
< 0.1%
1.95 × 10-61
< 0.1%
2.01 × 10-61
< 0.1%
2.18 × 10-61
< 0.1%
2.42 × 10-61
< 0.1%
2.58 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 15
0.1%
0.995 26
0.1%
0.994 20
0.1%
0.993 22
0.1%
0.992 14
0.1%
0.991 17
0.1%
0.99 13
0.1%
0.989 9
 
< 0.1%
0.988 10
 
0.1%
0.987 7
 
< 0.1%

danceability
Real number (ℝ)

Distinct849
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63334807
Minimum0
Maximum0.987
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:27.940720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.354
Q10.533
median0.645
Q30.748
95-th percentile0.876
Maximum0.987
Range0.987
Interquartile range (IQR)0.215

Descriptive statistics

Standard deviation0.15672271
Coefficient of variation (CV)0.24745114
Kurtosis-0.074796762
Mean0.63334807
Median Absolute Deviation (MAD)0.107
Skewness-0.39171912
Sum11929.111
Variance0.024562006
MonotonicityNot monotonic
2024-12-15T16:27:28.523301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.687 73
 
0.4%
0.694 73
 
0.4%
0.6 65
 
0.3%
0.657 65
 
0.3%
0.757 64
 
0.3%
0.611 64
 
0.3%
0.699 62
 
0.3%
0.646 62
 
0.3%
0.659 61
 
0.3%
0.71 59
 
0.3%
Other values (839) 18187
96.6%
ValueCountFrequency (%)
0 2
< 0.1%
0.0594 1
< 0.1%
0.0617 1
< 0.1%
0.0625 1
< 0.1%
0.066 1
< 0.1%
0.0674 1
< 0.1%
0.0684 1
< 0.1%
0.0722 1
< 0.1%
0.081 1
< 0.1%
0.0812 1
< 0.1%
ValueCountFrequency (%)
0.987 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 1
 
< 0.1%
0.978 3
< 0.1%
0.975 3
< 0.1%
0.972 1
 
< 0.1%
0.971 1
 
< 0.1%
0.97 1
 
< 0.1%
0.969 1
 
< 0.1%
0.968 2
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct1132
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64499476
Minimum0.00107
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:29.077568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.00107
5-th percentile0.237
Q10.51
median0.674
Q30.815
95-th percentile0.941
Maximum0.999
Range0.99793
Interquartile range (IQR)0.305

Descriptive statistics

Standard deviation0.21410076
Coefficient of variation (CV)0.33194186
Kurtosis-0.13787455
Mean0.64499476
Median Absolute Deviation (MAD)0.151
Skewness-0.62073751
Sum12148.476
Variance0.045839134
MonotonicityNot monotonic
2024-12-15T16:27:29.634876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.704 62
 
0.3%
0.63 58
 
0.3%
0.717 57
 
0.3%
0.73 53
 
0.3%
0.786 52
 
0.3%
0.805 52
 
0.3%
0.849 51
 
0.3%
0.694 51
 
0.3%
0.783 50
 
0.3%
0.785 49
 
0.3%
Other values (1122) 18300
97.2%
ValueCountFrequency (%)
0.00107 2
< 0.1%
0.00163 1
< 0.1%
0.00205 1
< 0.1%
0.00212 1
< 0.1%
0.00264 1
< 0.1%
0.00266 1
< 0.1%
0.00289 1
< 0.1%
0.00305 1
< 0.1%
0.00344 1
< 0.1%
0.00362 1
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.997 3
 
< 0.1%
0.996 6
 
< 0.1%
0.995 8
< 0.1%
0.994 10
0.1%
0.993 7
 
< 0.1%
0.992 8
< 0.1%
0.991 13
0.1%
0.99 19
0.1%
0.989 14
0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct3925
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078008039
Minimum0
Maximum0.997
Zeros7150
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:30.151551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.14 × 10-5
Q30.00257
95-th percentile0.782
Maximum0.997
Range0.997
Interquartile range (IQR)0.00257

Descriptive statistics

Standard deviation0.22159061
Coefficient of variation (CV)2.8406125
Kurtosis7.5636644
Mean0.078008039
Median Absolute Deviation (MAD)1.14 × 10-5
Skewness2.9851764
Sum1469.2814
Variance0.049102398
MonotonicityNot monotonic
2024-12-15T16:27:30.750724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7150
38.0%
3.33 × 10-637
 
0.2%
0.00107 21
 
0.1%
0.00114 20
 
0.1%
0.000512 18
 
0.1%
0.00103 17
 
0.1%
1.16 × 10-617
 
0.1%
4.91 × 10-616
 
0.1%
1.07 × 10-516
 
0.1%
1.03 × 10-516
 
0.1%
Other values (3915) 11507
61.1%
ValueCountFrequency (%)
0 7150
38.0%
1 × 10-62
 
< 0.1%
1.01 × 10-66
 
< 0.1%
1.02 × 10-68
 
< 0.1%
1.03 × 10-68
 
< 0.1%
1.04 × 10-612
 
0.1%
1.05 × 10-67
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-69
 
< 0.1%
1.08 × 10-65
 
< 0.1%
ValueCountFrequency (%)
0.997 1
 
< 0.1%
0.989 1
 
< 0.1%
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%
0.977 1
 
< 0.1%
0.975 1
 
< 0.1%
0.974 1
 
< 0.1%
0.973 3
< 0.1%
0.972 2
< 0.1%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2891956
Minimum0
Maximum11
Zeros2182
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size36.9 KiB
2024-12-15T16:27:31.273835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6145946
Coefficient of variation (CV)0.68339212
Kurtosis-1.3114659
Mean5.2891956
Median Absolute Deviation (MAD)3
Skewness-0.0025200197
Sum99622
Variance13.065294
MonotonicityNot monotonic
2024-12-15T16:27:31.653408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 2182
11.6%
1 2164
11.5%
7 2032
10.8%
2 1715
9.1%
9 1698
9.0%
11 1600
8.5%
5 1574
8.4%
6 1351
7.2%
8 1349
7.2%
10 1331
7.1%
Other values (2) 1839
9.8%
ValueCountFrequency (%)
0 2182
11.6%
1 2164
11.5%
2 1715
9.1%
3 512
 
2.7%
4 1327
7.0%
5 1574
8.4%
6 1351
7.2%
7 2032
10.8%
8 1349
7.2%
9 1698
9.0%
ValueCountFrequency (%)
11 1600
8.5%
10 1331
7.1%
9 1698
9.0%
8 1349
7.2%
7 2032
10.8%
6 1351
7.2%
5 1574
8.4%
4 1327
7.0%
3 512
 
2.7%
2 1715
9.1%

liveness
Real number (ℝ)

Distinct1425
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17965031
Minimum0.0109
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:32.034527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0109
5-th percentile0.0575
Q10.0929
median0.122
Q30.221
95-th percentile0.466
Maximum0.986
Range0.9751
Interquartile range (IQR)0.1281

Descriptive statistics

Standard deviation0.14398417
Coefficient of variation (CV)0.80146908
Kurtosis5.789919
Mean0.17965031
Median Absolute Deviation (MAD)0.042
Skewness2.2154227
Sum3383.7135
Variance0.02073144
MonotonicityNot monotonic
2024-12-15T16:27:32.787514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 219
 
1.2%
0.111 206
 
1.1%
0.104 197
 
1.0%
0.106 195
 
1.0%
0.109 190
 
1.0%
0.11 188
 
1.0%
0.102 187
 
1.0%
0.112 187
 
1.0%
0.107 186
 
1.0%
0.101 178
 
0.9%
Other values (1415) 16902
89.7%
ValueCountFrequency (%)
0.0109 1
 
< 0.1%
0.0119 1
 
< 0.1%
0.0148 1
 
< 0.1%
0.0157 1
 
< 0.1%
0.0186 1
 
< 0.1%
0.0193 1
 
< 0.1%
0.0196 3
< 0.1%
0.0206 1
 
< 0.1%
0.0215 1
 
< 0.1%
0.0219 1
 
< 0.1%
ValueCountFrequency (%)
0.986 1
< 0.1%
0.984 1
< 0.1%
0.983 1
< 0.1%
0.981 2
< 0.1%
0.979 1
< 0.1%
0.978 2
< 0.1%
0.977 1
< 0.1%
0.975 1
< 0.1%
0.974 1
< 0.1%
0.967 1
< 0.1%

loudness
Real number (ℝ)

High correlation 

Distinct8416
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.4474347
Minimum-38.768
Maximum1.585
Zeros0
Zeros (%)0.0%
Negative18828
Negative (%)> 99.9%
Memory size147.3 KiB
2024-12-15T16:27:33.138549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-38.768
5-th percentile-14.295
Q1-9.044
median-6.555
Q3-4.908
95-th percentile-3.149
Maximum1.585
Range40.353
Interquartile range (IQR)4.136

Descriptive statistics

Standard deviation3.8278312
Coefficient of variation (CV)-0.51397983
Kurtosis6.5224796
Mean-7.4474347
Median Absolute Deviation (MAD)1.923
Skewness-1.9295106
Sum-140272.43
Variance14.652292
MonotonicityNot monotonic
2024-12-15T16:27:33.466222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.589 24
 
0.1%
-4.206 20
 
0.1%
-9.127 19
 
0.1%
-6.35 18
 
0.1%
-5.144 18
 
0.1%
-6.439 18
 
0.1%
-8.304 17
 
0.1%
-5.991 17
 
0.1%
-5.593 17
 
0.1%
-5.351 17
 
0.1%
Other values (8406) 18650
99.0%
ValueCountFrequency (%)
-38.768 1
< 0.1%
-36.729 1
< 0.1%
-36.281 1
< 0.1%
-35.449 1
< 0.1%
-35.389 2
< 0.1%
-34.255 1
< 0.1%
-33.929 1
< 0.1%
-33.859 1
< 0.1%
-33.493 1
< 0.1%
-33.246 1
< 0.1%
ValueCountFrequency (%)
1.585 1
< 0.1%
1.342 1
< 0.1%
0.878 1
< 0.1%
0.525 1
< 0.1%
0.198 1
< 0.1%
0.119 1
< 0.1%
0.052 1
< 0.1%
-0.257 1
< 0.1%
-0.398 1
< 0.1%
-0.578 1
< 0.1%

audio_mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
11831 
0
7004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

Length

2024-12-15T16:27:33.776486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T16:27:34.017488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

Most occurring characters

ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11831
62.8%
0 7004
37.2%

speechiness
Real number (ℝ)

Distinct1224
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10209912
Minimum0
Maximum0.941
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:34.295644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0288
Q10.0378
median0.0555
Q30.119
95-th percentile0.334
Maximum0.941
Range0.941
Interquartile range (IQR)0.0812

Descriptive statistics

Standard deviation0.10437848
Coefficient of variation (CV)1.0223249
Kurtosis6.5049769
Mean0.10209912
Median Absolute Deviation (MAD)0.0227
Skewness2.271018
Sum1923.037
Variance0.010894867
MonotonicityNot monotonic
2024-12-15T16:27:34.602113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337 66
 
0.4%
0.032 61
 
0.3%
0.031 61
 
0.3%
0.0362 60
 
0.3%
0.038 59
 
0.3%
0.0318 58
 
0.3%
0.0329 58
 
0.3%
0.0439 56
 
0.3%
0.0457 55
 
0.3%
0.0333 55
 
0.3%
Other values (1214) 18246
96.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.0224 1
 
< 0.1%
0.0228 4
< 0.1%
0.0229 1
 
< 0.1%
0.0231 3
< 0.1%
0.0233 2
< 0.1%
0.0234 2
< 0.1%
0.0235 2
< 0.1%
0.0236 4
< 0.1%
0.0238 2
< 0.1%
ValueCountFrequency (%)
0.941 1
< 0.1%
0.94 1
< 0.1%
0.936 1
< 0.1%
0.915 1
< 0.1%
0.906 1
< 0.1%
0.894 1
< 0.1%
0.891 2
< 0.1%
0.89 1
< 0.1%
0.869 2
< 0.1%
0.831 2
< 0.1%

tempo
Real number (ℝ)

Distinct12112
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.07315
Minimum0
Maximum242.318
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:34.923112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.4165
Q198.368
median120.013
Q3139.931
95-th percentile174.1473
Maximum242.318
Range242.318
Interquartile range (IQR)41.563

Descriptive statistics

Standard deviation28.714456
Coefficient of variation (CV)0.23716617
Kurtosis-0.21751669
Mean121.07315
Median Absolute Deviation (MAD)20.049
Skewness0.44285458
Sum2280412.9
Variance824.51997
MonotonicityNot monotonic
2024-12-15T16:27:35.272641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125.978 20
 
0.1%
95.948 19
 
0.1%
120.013 18
 
0.1%
97.064 18
 
0.1%
123.07 17
 
0.1%
147.055 17
 
0.1%
89.931 16
 
0.1%
128.038 16
 
0.1%
104.053 16
 
0.1%
118.159 16
 
0.1%
Other values (12102) 18662
99.1%
ValueCountFrequency (%)
0 2
< 0.1%
46.591 1
< 0.1%
47.953 1
< 0.1%
51.607 1
< 0.1%
52.181 1
< 0.1%
54.213 1
< 0.1%
56.983 1
< 0.1%
56.985 1
< 0.1%
57 1
< 0.1%
57.107 1
< 0.1%
ValueCountFrequency (%)
242.318 1
< 0.1%
216.115 1
< 0.1%
214.686 1
< 0.1%
213.99 1
< 0.1%
213.226 1
< 0.1%
212.137 1
< 0.1%
212.058 1
< 0.1%
211.644 1
< 0.1%
211.357 1
< 0.1%
210.75 1
< 0.1%

time_signature
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
4
17754 
3
 
772
5
 
233
1
 
73
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18835
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

Length

2024-12-15T16:27:35.560179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T16:27:35.802360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 17754
94.3%
3 772
 
4.1%
5 233
 
1.2%
1 73
 
0.4%
0 3
 
< 0.1%

audio_valence
Real number (ℝ)

Distinct1246
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52796688
Minimum0
Maximum0.984
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2024-12-15T16:27:36.107124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132
Q10.335
median0.527
Q30.725
95-th percentile0.924
Maximum0.984
Range0.984
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.24463169
Coefficient of variation (CV)0.46334666
Kurtosis-0.97767048
Mean0.52796688
Median Absolute Deviation (MAD)0.195
Skewness-0.016423217
Sum9944.2561
Variance0.059844663
MonotonicityNot monotonic
2024-12-15T16:27:36.441457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 71
 
0.4%
0.964 61
 
0.3%
0.376 53
 
0.3%
0.505 53
 
0.3%
0.591 52
 
0.3%
0.962 51
 
0.3%
0.329 48
 
0.3%
0.493 46
 
0.2%
0.422 42
 
0.2%
0.319 42
 
0.2%
Other values (1236) 18316
97.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.023 1
< 0.1%
0.0243 1
< 0.1%
0.0277 1
< 0.1%
0.0292 2
< 0.1%
0.0301 1
< 0.1%
0.0309 1
< 0.1%
0.0312 1
< 0.1%
0.0316 1
< 0.1%
0.032 2
< 0.1%
ValueCountFrequency (%)
0.984 1
 
< 0.1%
0.982 3
< 0.1%
0.981 2
 
< 0.1%
0.98 3
< 0.1%
0.979 2
 
< 0.1%
0.978 5
< 0.1%
0.977 4
< 0.1%
0.976 5
< 0.1%
0.975 6
< 0.1%
0.974 6
< 0.1%

Interactions

2024-12-15T16:27:20.345465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:41.320119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:44.281904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:48.290357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:51.217949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:54.210610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:56.928856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:01.004086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:07.230731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:10.109250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:13.042915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:16.977005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:20.604303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:41.539514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:44.518398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:48.671004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:51.436362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:54.426838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:57.141510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:01.505627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:07.473863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:10.334228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:13.322028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:17.336423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:20.868545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:41.807131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:44.842049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:48.952214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:51.658622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:54.652483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:57.392850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:02.298228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:07.755580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:10.583417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:13.678066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:18.030521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:21.095365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:42.034492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:45.189665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:49.183651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:52.145633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:54.887587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:57.613743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:03.039964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:08.007711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:10.829356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:13.975262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:18.246688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:21.317691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:42.259199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:45.522434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:49.399311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:52.362641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:55.097937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:57.849355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:03.926811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:08.224692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:11.076290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:14.281256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:18.498902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:21.550865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:42.475631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:45.891875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:49.606735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:52.579344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:55.334244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:58.070596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:04.320970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:08.448174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:11.314697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:14.568644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:18.729415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:21.788089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:42.714517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:46.261777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:49.844825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:52.810029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:55.559804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:58.280282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:04.675727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:08.677243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:11.544666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:14.921189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:18.972816image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:22.021808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:42.947561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:46.566415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:50.068540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:53.030287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:55.782540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:58.506032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:05.113738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:08.943660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:11.797700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:15.261337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:19.187452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:22.242482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:43.175387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:46.914163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:50.278955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:53.261403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:56.007898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:58.746993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:05.578364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:09.159148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:12.062658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:15.575228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:19.412513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:22.488930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:43.609564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:47.254082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:50.531994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:53.500619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:56.252516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:59.117821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:06.101254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:09.400422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:12.331359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:15.922493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:19.662863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:22.736473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:43.843874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:47.582433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:50.740235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:53.717641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:56.482395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:59.633672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:06.461546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:09.618410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:12.562181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:16.245163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:19.900203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:22.955594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:44.054568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:47.927390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:50.983164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:53.981354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:26:56.692253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:00.231116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:06.812567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:09.872737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:12.805514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:16.603926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-15T16:27:20.122724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-15T16:27:36.666685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
acousticnessaudio_modeaudio_valencedanceabilityenergyinstrumentalnesskeylivenessloudnesssong_duration_mssong_popularityspeechinesstempotime_signature
acousticness1.0000.073-0.040-0.043-0.591-0.028-0.001-0.069-0.454-0.001-0.037-0.140-0.1670.121
audio_mode0.0731.0000.0240.1120.0690.0170.2770.0270.0590.0190.0360.1140.0230.022
audio_valence-0.0400.0241.0000.3160.286-0.1320.024-0.0280.1520.006-0.0500.0420.0250.079
danceability-0.0430.1120.3161.000-0.035-0.1250.011-0.1070.0850.0030.1010.288-0.0920.179
energy-0.5910.0690.286-0.0351.000-0.0560.0190.1320.7240.0210.0040.1820.1630.119
instrumentalness-0.0280.017-0.132-0.125-0.0561.000-0.005-0.047-0.2640.003-0.201-0.198-0.0040.054
key-0.0010.2770.0240.0110.019-0.0051.000-0.0020.0120.008-0.0150.0480.0040.016
liveness-0.0690.027-0.028-0.1070.132-0.047-0.0021.0000.093-0.005-0.0330.0710.0310.024
loudness-0.4540.0590.1520.0850.724-0.2640.0120.0931.0000.0120.1370.1810.1170.085
song_duration_ms-0.0010.0190.0060.0030.0210.0030.008-0.0050.0121.000-0.0130.0010.0070.021
song_popularity-0.0370.036-0.0500.1010.004-0.201-0.015-0.0330.137-0.0131.0000.040-0.0190.025
speechiness-0.1400.1140.0420.2880.182-0.1980.0480.0710.1810.0010.0401.0000.0780.062
tempo-0.1670.0230.025-0.0920.163-0.0040.0040.0310.1170.007-0.0190.0781.0000.413
time_signature0.1210.0220.0790.1790.1190.0540.0160.0240.0850.0210.0250.0620.4131.000

Missing values

2024-12-15T16:27:23.290546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T16:27:23.827202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
0Boulevard of Broken Dreams731890.0055200.4960.6820.00002980.0589-4.09510.0294167.06040.474
1In The End66203250.0103000.5420.8530.00000030.1080-6.40700.0498105.25640.370
2Seven Nation Army76-304110.0081700.7370.4630.44700000.2550-7.82810.0792123.88140.324
3By The Way74203250.0264000.4510.9700.00355000.1020-4.93810.1070122.44440.198
4How You Remind Me56272180.0009540.4470.7660.000000100.1130-5.06510.0313172.01140.574
5Bring Me To Life80-262510.0089500.3160.9450.00000240.3960-3.16900.1240189.93140.320
6Last Resort8132850.0005040.5810.8870.00111040.2680-3.65900.062490.57840.724
7Are You Gonna Be My Girl76171920.0014800.6130.9530.00058220.1520-3.43510.0855105.04640.537
8Mr. Brightside80259780.0010800.3300.9360.00000010.0926-3.66010.0917148.11240.234
9Sex on Fire8167380.0017200.5420.9050.01040090.1360-5.65310.0540153.39840.374
song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
18825Something Familiar60143940.9060.4910.04090.00001500.0896-18.43110.0383131.05330.2780
18826Call It Dreaming67-303840.6100.5190.51500.00005750.1070-9.44810.031080.32940.7140
18827Stay Awake55-164900.8980.3700.13600.00026370.0999-13.52810.0433146.08140.0592
18828Build Me Up From Bones64195650.8620.5150.28600.00006950.1060-11.77610.0378115.07640.2840
18829I Know62-15020.3950.6440.52300.00000040.0930-7.66010.037895.96640.4450
18830Let It Breathe60285730.8930.5000.15100.000065110.1110-16.10710.0348113.96940.3000
18831Answers6090580.7650.4950.16100.000001110.1050-14.07800.030194.28640.2650
18832Sudden Love (Acoustic)23-143970.8470.7190.32500.00000000.1250-12.22210.0355130.53440.2860
18833Gentle on My Mind55246000.9450.4880.32600.01570030.1190-12.02010.0328106.06340.3230
18834Up to Me60-30750.9110.6400.38100.00025440.1040-11.79010.030291.49040.5810

Duplicate rows

Most frequently occurring

song_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence# duplicates
586FEFE (feat. Nicki Minaj & Murda Beatz)96-172040.088000.9310.3870.00000010.1360-9.12710.4120125.97840.37619
1167MIA (feat. Drake)94137590.014300.8180.5400.00051260.0990-6.35000.054497.06440.17418
1811Taki Taki (with Selena Gomez, Ozuna & Cardi B)98158920.153000.8410.7980.00000310.0618-4.20600.229095.94840.59118
1362No Stylist91-44360.021500.7650.7040.00000050.2270-4.58900.1270147.05540.49817
561Electricity (with Dua Lipa)94-239710.010400.5880.6700.00000300.3380-6.43910.0473118.15940.50516
864I Love It (& Lil Pump)99-31260.011400.9010.5220.00000020.2590-8.30410.3300104.05340.32916
1481Promises (with Sam Smith)98167010.011900.7810.7680.000005110.3250-5.99110.0394123.07040.48616
1773Sunflower - Spider-Man: Into the Spider-Verse66269810.541000.7570.5010.00000020.0718-5.59310.046689.93140.91016
196Be Alright96-2350.697000.5530.5860.000000110.0813-6.31910.0362126.68440.44314
2038Wake Up in the Sky9280560.003810.8000.5780.00000040.3590-5.14400.0485143.01040.36714